Weighted least squares is a regression technique that accounts for the different variances of observations by assigning weights to each data point in the fitting process. This approach is particularly useful when dealing with heteroscedasticity, where the variability of the errors differs across observations. By applying appropriate weights, this method ensures that more reliable observations have a greater influence on the estimated coefficients.
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